On completion of the programme the candidate should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The candidate

• is familiar with the whole data science pipeline including preprocessing, data management analysis, learning, visualization, reporting, and operationalization
• understands the data-generation process
• have theoretical understanding of potential and limitations of data science methods
• have solid knowledge on mathematics and statistics
• have in-depth knowledge in one specific subfield of data science or application area of data science

Skills

The candidate

• can design and manage data science projects
• is able to choose appropriate tools and apply them in real-life scenarios
• can interpret the results of data science pipelines
• is able to implement complex computer programs

General competence

The candidate

• Is able to work independently and in groups with advanced issues within the field of data science.
• Critical and analytical view for own and others work
• Able to extend own knowledge themselves
• Be able to assess ethical aspects of the work
• Can write reports and give oral presentations, following good scientific practice

The programme has two components: a subject part of 270 credits and an individual master's thesis,

DSC399K ¿ Master thesis in Data Science, of 30 credits. The master's thesis is an independent scientific work, which is carried out under the guidance of a supervisor.

The programme contains two specializations: general data science and medical data science. Students must choose to follow one of the two specializations before the end of their 4th semester.

1.semester (Fall)

General Data Science / Medical Data Science:

MAT111 or MAT105 ¿ INF100 ¿ INF140

2. semester (Spring)

General Data Science / Medical Data Science:

MAT121 ¿ MNF130 ¿ INF101

3.semester (Fall)

General Data Science / Medical Data Science:

STAT110 - INF161 - INF102

4. semester (Spring)

General Data Science / Medical Data Science:

INF115 - INF112 - STAT200

5. semester (Fall)

General Data Science: PHYS116 - INF170 ¿ INNOV201

Medical Data Science: INNOV201 - INF170 - Ex.phil

6. semester (Spring)

General Data Science: Ex.phil ¿ Elective course* - Elective course*

Medical Data Science: KJEM220 - BINF100 - Anatomi og fysiologi

7. semester (Fall)

General Data Science / Medical Data Science:

INF234 - INF264 -AIKI210

8. semester (Spring)

General Data Science / Medical Data Science:

DSC219 - INF250 - Elective course* **

9. semester (Fall)

General Data Science: DSC300 - Elective course* - Elective course*

Medical Data Science: DSC300 - DSC316 (20 credits)

10. semester (Spring)

General Data Science / Medical Data Science:

DSC399K (Master¿s thesis)

*The specialization General Data Science contains 50 credits in elective courses, of which at least 20 credits must be engineering course in accordance with the requirements for the degree. You choose elective courses after 6th semester in agreement with your supervisor.

** The specialization Medical Data Science contains 10 credits in elective courses that you must choose in agreement with supervisor.

The following courses are compulsory for students who follow the specialization General Data Science:

INF100, INF140, MAT105/MAT111, MAT121, MNF130, INF101, STAT110, INF161, INF102, INF115, INF112, STAT200, PHYS116, INF170, INNOV201, INF234, INF264, AIKI210, DSC219, INF250, DSC300 og DSC399K. At least 20 credits of the elective courses must be engineering course, in accordance with the requirements for the degree.

The following courses are compulsory for students who follow the specialization Medical Data Science:

INF100, INF140, MAT105/MAT111, MAT121, MNF130, INF101, STAT110, INF16,1 INF102, INF115, INF112, STAT200, INF170, INNOV201, KJEM220, BINF100, Anatomy and physiology, INF234, INF264, AIKI210, DSC219, INF250, DSC300, DSC316 og DSC399K.

Maximum 10 credits in course on 100 level is allowed in the degree after and including the 7th semester.

General data science:

DSC316 is recommended as an elective course for students who follow this specialization.

This specialization has 50 credits in elective courses, of which at least 20 credits must be linked to one of the pre-approved course combinations below.

• Visual Data Science: INF252, INF253
• Statistical Data Science: STAT220, STAT250
• Algorithms: INF236, INF237
• Biological Data Science: BINF100, BINF200
• Economics/Optimization: INF270, INF271
• Networks/graphs: INF237, INF207
• Machine learning: INF265, INF367

The list above is considered a starting point for possible course combinations and may be updated. Students can apply to the program committee to take courses not included in the list of pre-approved courses. Courses must meet the requirements for the degree.

Medical data science:

Contains 10 credits in elective courses.

At UiB the grades are given in one of two possible grading scales: passed/failed and A to F. The master's thesis will be graded A to F.

The grading scale for each course is given in the course description.

According to the US Bureau of Labor Statistics, data science jobs are some of the fastest growing, most in demand technology. Data scientists work in:

• Medicine
• Finance, bank and insurance
• High-tech companies
• IT og telecommunication
• Consultant-business
• Entrepreneurship
• Public administration and research
• Large industries